diff --git a/stanza/models/mwt/data.py b/stanza/models/mwt/data.py
index b8b0b2f1e..791c23efa 100644
--- a/stanza/models/mwt/data.py
+++ b/stanza/models/mwt/data.py
@@ -3,6 +3,7 @@
 import os
 from collections import Counter
 import logging
+
 import torch
 
 import stanza.models.common.seq2seq_constant as constant
@@ -17,7 +18,9 @@
 # including covering some potential " typos
 # setting the augmentation to a very low value should be enough to teach it
 # about the unknown characters without messing up the predictions for other text
-APOS = ('"', '’', 'ʼ')
+#
+# 0x22, 0x27, 0x02BC, 0x02CA, 0x2019
+APOS = ('"', "'", 'ʼ', 'ˊ', '’')
 
 # TODO: can wrap this in a Pytorch DataLoader, such as what was done for POS
 class DataLoader:
@@ -34,7 +37,7 @@ def __init__(self, doc, batch_size, args, vocab=None, evaluation=False, expand_u
         if vocab is None:
             assert self.evaluation == False # for eval vocab must exist
             self.vocab = self.init_vocab(data)
-            if self.augment_apos > 0 and "'" in self.vocab:
+            if self.augment_apos > 0 and any(x in self.vocab for x in APOS):
                 for apos in APOS:
                     self.vocab.add_unit(apos)
         elif expand_unk_vocab:
@@ -66,9 +69,12 @@ def init_vocab(self, data):
         return vocab
 
     def maybe_augment_apos(self, datum):
-        if "'" in datum[0] and random.uniform(0,1) < self.augment_apos:
-            replacement = random.choice(APOS)
-            datum = (datum[0].replace("'", replacement), datum[1].replace("'", replacement))
+        for original in APOS:
+            if original in datum[0]:
+                if random.uniform(0,1) < self.augment_apos:
+                    replacement = random.choice(APOS)
+                    datum = (datum[0].replace(original, replacement), datum[1].replace(original, replacement))
+                break
         return datum